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MiMo V2.5 Pro Thinking

xiaomi/mimo-v2.5-pro:thinking
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MiMo V2.5 Pro Thinking

xiaomi/mimo-v2.5-pro:thinking

MiMo V2.5 Pro with Xiaomi thinking enabled for coding, long-context reasoning, and agentic orchestration.

Added Jun 3, 2026

Model weights

Context Window

1.0M

Max Output

131.1K

Avg output tokens (7d)

1.3K tokens

77%

Input Price (Auto)

$0.43/1M

Output Price (Auto)

$0.87/1M

Cache Read (Auto)

$0.0036/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

26.4

Better than 81% of models compared

Coding Index

60.2

Better than 73% of models compared

Agentic Index

22.7

Better than 63% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

13.7%

Better than 41% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

883 Elo

Better than 47% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

1186 Elo

Better than 63% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

4.0%

Better than 28% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

79.7%

Better than 88% of models compared

Reasoning

HLE

Humanity's Last Exam

35.7%

Better than 87% of models compared

IFBench

Instruction-following benchmark

79.9%

Better than 98% of models compared

CritPt

Research-level physics reasoning

4.0%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 17% of models compared

SciCode

Python programming for scientific computing

50.6%

Better than 61% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

22.4%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

24.7%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

86.6%

Better than 81% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

43.2%

Better than 90% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

94.2%

Better than 92% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

79.7%

Better than 88% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

34.3%

Last updated Sep 12, 2026

Artificial Analysis

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